SVG path analysis in R
JG Pardyak
7/22/2022
Scalable Vector Graphics, abbreviated: SVG, is an XML-based file format for static and dynamic vector graphics. It is an open standard of the W3C. Inkscape is an open source vector illustration program that uses the SVG format to store vector graphics. We start our analyses from checking Inkscape installation
system('inkscape --version', intern = TRUE)## [1] "Inkscape 1.0.2-2 (e86c870879, 2021-01-15)"is_url <- function(path){
grepl("^https?://", path)
}
download_svg <- function(url){
filename = tempfile("inx", fileext = ".svg")
download.file(url, filename)
return(filename)
}Function to convert SVG into DXF
inx_extension <- function(input, extension, ext){
path = system('inkscape --system-data-directory', intern = TRUE)
inkscape_extensions_path = paste(path, "\\extensions", sep = "")
inkscape_python_home = paste(gsub("\\share\\inkscape", "", path, fixed = T), "\\bin", sep = "")
if(is_url(input)) {
input_file_path = download_svg(input)
} else {
input_file_path = tempfile("inx")
file.copy(input, input_file_path)
}
output = tempfile("inx", fileext = ext)
command = tempfile(pattern = "inx_", fileext = ".bat")
'@ECHO OFF
cd %s
python.exe "%s" --output="%s" "%s"' %>% sprintf(
inkscape_python_home,
paste(inkscape_extensions_path, extension, sep = "\\"),
output,
input_file_path) %>% writeLines(command)
system(command)
output
}with it’s Linux equivalent:
inx_extension <- function(input, extension, ext){
path = system('inkscape --system-data-directory', intern = TRUE)
inkscape_extensions_path = paste(path, "/extensions", sep = "")
if(is_url(input)) {
input_file_path = download_svg(input)
} else {
input_file_path = tempfile("inx")
file.copy(input, input_file_path)
}
output <- tempfile("inx", fileext = ext)
command <- sprintf('python %s --output="%s" "%s"', paste(inkscape_extensions_path, extension, sep = "/"), output, input_file_path)
system(command, intern = TRUE)
output
}url <- 'https://upload.wikimedia.org/wikipedia/commons/1/1b/Red_Bird.svg'
#browseURL(url)knitr::include_graphics(url)
logo <- inx_extension(input = url, extension = "dxf12_outlines.py", ext =".dxf") %>%
st_read() %>%
select(geometry) %>% st_union() %>% st_polygonize() %>%
first()## Reading layer `entities' from data source
## `C:\Users\jacek\AppData\Local\Temp\RtmpABxEx6\inx45c0780e3606.dxf'
## using driver `DXF'
## Simple feature collection with 8654 features and 6 fields
## Geometry type: LINESTRING
## Dimension: XY
## Bounding box: xmin: 238.9009 ymin: -837.4039 xmax: 1105.521 ymax: 40.07607
## CRS: NAlogo %>% ggplot() +
geom_sf()
result <- st_sfc() %>% st_sf(geometry = .)
for(i in c(1: length(logo))) {
tmp <- logo %>%
nth(i) %>%
st_sfc() %>% st_sf(geometry = .) %>% mutate(facet = i)
result <- tmp %>% bind_rows(result)
}
result %>% plot_ly(split = ~facet)## No trace type specified:
## Based on info supplied, a 'scatter' trace seems appropriate.
## Read more about this trace type -> https://plotly.com/r/reference/#scatterresult %>% ggplot() +
geom_sf() +
geom_sf_label(aes(label = facet)) +
theme_void()
logo <- result %>% filter(!facet %in% c(5, 10, 38, 50:52, 56:60))
logo %>% ggplot() +
geom_sf() +
theme_void()
grid_spacing = 10
grid <- result %>% st_make_grid(what = "centers", cellsize = c(grid_spacing, grid_spacing)) %>%
st_sf() grid %>% ggplot() +
geom_sf() +
theme_void()
heights <- st_join(grid, (result %>% select(geometry) %>% mutate(Z = 1))) %>% replace(is.na(.), 0) surface <- heights %>% st_coordinates() %>% as_tibble() %>%
bind_cols(heights %>% st_drop_geometry()) %>%
mutate(X = round(X,1)) %>%
mutate(Y = round(Y,1)) %>% pivot_wider(names_from = Y, values_from = Z) %>%
column_to_rownames("X") %>% as.matrix()
surface## -832.4 -822.4 -812.4 -802.4 -792.4 -782.4 -772.4 -762.4 -752.4 -742.4
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